The 3 Worst Tools for Analytics in Commercial Real Estate

As a real estate investor, lender, developer, or broker, your data is your competitive edge. You gather it from brokers, tenant conversations, newspaper articles, OMs, third party data providers, and many other sources. But after gathering the data, it sits unused in your company’s shared server or remains disconnected across your team members’ collection of emails, spreadsheets, and PDFs.

That is, until someone needs an analysis — then the race is on to find, format, process, clean, and present the data! The three tools listed below are common ways real estate firms attempt to produce “analytics” but they fail to provide a unified, efficient way to dig deeply into data, quickly identify trends, and share intel across your firm. Fortunately, at the end of this article we discuss the best way to implement analytics across your real estate firm.

Excel

Excel is the most commonly used tool to maintain “databases” of information, whether sales comps, lease comps, or other lists of data. In theory, using Excel for this record keeping is a good idea as it is a ubiquitous software that everyone in an organization knows how to use. Yet there are significant drawbacks to maintaining your information in Excel.

First, each person can maintain their own copy of the spreadsheet. If there’s isn’t a centralized, easily-accessible location for all team members to access information, each person could be working from her or his own cut of the data, so it’s unclear which version is the most recent, most complete, or most correct.

Second, seeing real estate data in Excel is not the same as seeing it on a map. Because real estate relies on understanding information in physical proximity, you need to see your information on a street grid, rather than Excel’s grid of rows and columns.

Excel is the most flexible and effective tool for real estate underwriting and other modeling. But if you want to build a robust research and analytics infrastructure, Excel is the most basic tool that gives teams the illusion of capturing information while all it really does is store disconnected data points in the “dusty basement” of your organization’s server.

BatchGeo

BatchGeo is a very valuable tool for one purpose: making one-off, static maps. But standalone maps are not an analytics solution. Though it’s simple to create a BatchGeo map for a single dataset, if you want to take a look at a second cut of the data or dig deeper into a specific data point, you need to go back to your data source and likely create an entirely new presentation. You cannot run analyses or alternative scenarios on the fly. Furthermore, if you make a map and then revisit it a month or even a year later, it’s highly likely you’d have to recreate the underlying data and presentation nearly from scratch. This slows down decision-making and wastes time, forcing team members to manage data rather than refine investment underwritings and metrics.

Notebooks, Emails, and Human Memory

There is no doubt that possessing institutional memory is crucial to real estate success. But how many data points can you keep in your memory? You remember the story behind the sale of that building down the street and the leases in a competitor’s building that were signed because tenants were offered extra TI money. However, for many questions, you have to find the right person — the person who actually dealt with that transaction — which can take hours or even days.

Furthermore, what about the data that slips your mind? It’s impossible to remember every rent roll of every building you’ve ever looked at; similarly, it’s impossible to know all the sale comps in a submarket in the past two years. So how can you augment your team’s collective institutional knowledge with a system that integrates all data points you’ve gathered on the companies, buildings, competitors, and markets in your investment universe?

Predictre solves the issues associated with all three tools mentioned in this article. It’s a collaborative platform just for your team that allows you to join together various data sources, whether a spreadsheet of sales comps or an anecdote you get from a phone call. And it’s map-based because real estate data needs to be seen in its spatial context of streets, city blocks, and submarkets. If you’re interested in learning how to augment your team’s collective institutional knowledge with cutting-edge, map-based analytics, we look forward to hearing from you at: info@predictre.ai.